Effects of length distribution on the steady shear viscosity of semiconcentrated polymer‐fiber suspensions
Bibliographic record
Abstract
Abstract The steady state shear viscosity of glass fibers in a polyethylene oxide polymer solution was determined. The experiments were conducted for milled fibers that show a wide distribution of aspect ratios as well as for fiber samples that have a uniform and well‐defined aspect ratio. In particular, results for one‐modal, bi‐modal, and tri‐modal samples obtained by mixing fibers of uniform lengths were examined. These results allowed us to analyze the effect of the aspect ratio distribution on the viscosity and to determine the validity of different conventional averaging techniques that have been used so far to characterize the aspect ratio of milled fibers. It was found that all these conventional averaging methods do not give a good representation of the effect of the fibers length and that the widely used number‐averaging technique fails to account for the important contribution of the long fibers vs. the short ones. A new model‐based averaging method is proposed and its performance compared to that of other conventional averaging techniques is discussed. POLYM. ENG. SCI., 45:1357–1368, 2005. © 2005 Society of Plastics Engineers
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".